CS 301 Lecture 17 – Church–Turing thesis Stephen Checkoway March 19, 2018 1 / 17
An abridged modern history of formalizing algorithms An algorithm is a finite, unambiguous sequence of steps for solving a problem 1 Polynomial equation with integer coefficients 2 Statements with ∀ and ∃ 2 / 17
An abridged modern history of formalizing algorithms An algorithm is a finite, unambiguous sequence of steps for solving a problem The name algorithm comes from 9th Century Iranian mathematician Muh .ammad ibn M¯ us¯ a al’Khw¯ arizm¯ ı 1 Polynomial equation with integer coefficients 2 Statements with ∀ and ∃ 2 / 17
An abridged modern history of formalizing algorithms An algorithm is a finite, unambiguous sequence of steps for solving a problem The name algorithm comes from 9th Century Iranian mathematician Muh .ammad ibn M¯ us¯ a al’Khw¯ arizm¯ ı In the 17th century, German mathematician Gottfried Leibniz wanted to build a machine that could determine if mathematical statements were true or false 1 Polynomial equation with integer coefficients 2 Statements with ∀ and ∃ 2 / 17
An abridged modern history of formalizing algorithms An algorithm is a finite, unambiguous sequence of steps for solving a problem The name algorithm comes from 9th Century Iranian mathematician Muh .ammad ibn M¯ us¯ a al’Khw¯ arizm¯ ı In the 17th century, German mathematician Gottfried Leibniz wanted to build a machine that could determine if mathematical statements were true or false In 1900, German mathematician David Hilbert posed a list of 23 open problems, the tenth asks for a general algorithm by which any Diophantine equation 1 could be solved 1 Polynomial equation with integer coefficients 2 Statements with ∀ and ∃ 2 / 17
An abridged modern history of formalizing algorithms An algorithm is a finite, unambiguous sequence of steps for solving a problem The name algorithm comes from 9th Century Iranian mathematician Muh .ammad ibn M¯ us¯ a al’Khw¯ arizm¯ ı In the 17th century, German mathematician Gottfried Leibniz wanted to build a machine that could determine if mathematical statements were true or false In 1900, German mathematician David Hilbert posed a list of 23 open problems, the tenth asks for a general algorithm by which any Diophantine equation 1 could be solved In 1928, Hilbert posed 3 more problems, the third of which became known as Hilbert’s Entscheidungsproblem (decision problem). It asks for an algorithm which takes as input a statement in first-order logic 2 1 Polynomial equation with integer coefficients 2 Statements with ∀ and ∃ 2 / 17
Formalizing algorithms In 1933, Austrian-American mathematician Kurt Gödel and French mathematician Jacques Herbrand 3 defined the class of general recursive functions; these were designed to be intuitively “computable” 3 Herbrand had actually died at age 23 several years earlier 4 Functional programming owes much to λ -calculus and lambdas and closures in many programming languages derive from this 3 / 17
Formalizing algorithms In 1933, Austrian-American mathematician Kurt Gödel and French mathematician Jacques Herbrand 3 defined the class of general recursive functions; these were designed to be intuitively “computable” In 1936, American mathematician Alonzo Church defined the λ -calculus as a model of computable functions 4 3 Herbrand had actually died at age 23 several years earlier 4 Functional programming owes much to λ -calculus and lambdas and closures in many programming languages derive from this 3 / 17
Formalizing algorithms In 1933, Austrian-American mathematician Kurt Gödel and French mathematician Jacques Herbrand 3 defined the class of general recursive functions; these were designed to be intuitively “computable” In 1936, American mathematician Alonzo Church defined the λ -calculus as a model of computable functions 4 Independently, in 1936, English mathematician Alan Turing defined his machines 3 Herbrand had actually died at age 23 several years earlier 4 Functional programming owes much to λ -calculus and lambdas and closures in many programming languages derive from this 3 / 17
Formalizing algorithms In 1933, Austrian-American mathematician Kurt Gödel and French mathematician Jacques Herbrand 3 defined the class of general recursive functions; these were designed to be intuitively “computable” In 1936, American mathematician Alonzo Church defined the λ -calculus as a model of computable functions 4 Independently, in 1936, English mathematician Alan Turing defined his machines Church and Turing proved that all three of these models of computation are equivalent: A function is λ -computable iff it is computable by a Turing machine iff it is general recursive 3 Herbrand had actually died at age 23 several years earlier 4 Functional programming owes much to λ -calculus and lambdas and closures in many programming languages derive from this 3 / 17
Church–Turing thesis The Church–Turing thesis is that the intuitive notion of an algorithm (called an “effectively calculable function”) is equivalent to a Turing machine 4 / 17
Hilbert’s problems Hilbert framed his tenth problem in 1900 and his Entscheidungsproblem in 1928 as a positive: Give an algorithm to solve the problems As late as 1930, he didn’t seem to realize that there would be any unsolvable problems 5 / 17
Hilbert’s problems Hilbert framed his tenth problem in 1900 and his Entscheidungsproblem in 1928 as a positive: Give an algorithm to solve the problems As late as 1930, he didn’t seem to realize that there would be any unsolvable problems Unfortunately (depending on your point of view), both of these problems don’t have general solutions 5 / 17
Types of problems There are several types of problems we could consider, consider the problem of paths in a weighted, undirected graph G between two vertices u and v Decision Is there some path between u and v in G of distance at most k ? Search What is the length of the shortest path between u and v in G ? Counting How many paths between u and v in G have distance at most k ? In this course, we’re concerned with the simplest of these: decision problems 6 / 17
Decision problems as languages We can frame decision problems as membership in a language. What language corresponds to the decision problem: Is there some path between u and v in graph G of distance at most k ? 7 / 17
Decision problems as languages We can frame decision problems as membership in a language. What language corresponds to the decision problem: Is there some path between u and v in graph G of distance at most k ? {⟨ G, u, v, k ⟩ ∣ G = ( V, E ) is an undirected graph, u, v ∈ V , and there is a path from u to v of distance at most k } ⟨ G, u, v, k ⟩ means some fixed representation of the graph G , vertices u and v , and natural number k 7 / 17
Representation For the rest of the course, we’re going to be working with languages whose elements are representations of mathematical objects like • integers • graphs • finite automata • regular expressions • CFGs • PDAs • Turing machines • finite sets of mathematical objects • finite, ordered lists of mathematical objects We need some way to represent these as strings over some alphabet 8 / 17
Explicit representation Most of the time, we won’t give an explicit representation We’ll mostly assume some representation exists and we can convert from one representation to another easily 9 / 17
Example: Graph representation As a mathematical object, an undirected graph 5 is a pair G = ( V, E ) where V a finite set of vertices E a finite set of pairs of vertices We can specify the graph in a number of ways and easily convert 3 between them 2 4 • Vertex and edge lists G = ( V, E ) where V = { 1 , 2 , 3 , 4 , 5 } and E = {( 1 , 2 ) , ( 2 , 3 ) , ( 3 , 4 ) , ( 4 , 5 ) , ( 5 , 1 ) , ( 1 , 3 ) , ( 2 , 4 ) , ( 3 , 5 )} 1 5 5 Review Sipser, chapter 0 as needed 10 / 17
Example: Graph representation As a mathematical object, an undirected graph 5 is a pair G = ( V, E ) where V a finite set of vertices E a finite set of pairs of vertices We can specify the graph in a number of ways and easily convert 3 between them 2 4 • Vertex and edge lists G = ( V, E ) where V = { 1 , 2 , 3 , 4 , 5 } and E = {( 1 , 2 ) , ( 2 , 3 ) , ( 3 , 4 ) , ( 4 , 5 ) , ( 5 , 1 ) , ( 1 , 3 ) , ( 2 , 4 ) , ( 3 , 5 )} 1 5 • Adjacency matrix a ij = 1 if ( i, j ) ∈ E ⎡ ⎤ 0 1 1 0 1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 1 0 1 1 0 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 1 1 0 1 1 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ 0 1 1 0 1 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 1 0 1 1 0 ⎣ ⎦ 5 Review Sipser, chapter 0 as needed 10 / 17
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